Some datasets in ecology (e.g. CPUE) inherently contain abundant zero values, which may need to be adjusted and/or fitted. Apart from common solutions, I am asking how to work with such kind of data in PRIMER-e (i.e. possible pretreatments, adjustments or other post-treatment functions).
You can use the zero adjusted Bray-Curtis index available at PRIMER which is made to deal with this problem.
Check this: https://doi.org/10.1016/j.jembe.2005.12.017
It depends on whether there is information in the zero values themselves - one potential approach is to include a dummy variable to assign meaning to the joint absence of species. See the following paper:
Clarke, K.R., Somerfield, P.J., Chapman, M.G., 2006. On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray–Curtis coefficient for denuded assemblages. J. Exp. Mar. Biol. Ecol. 330, 55-80.
Bit late to the party, but effectively using the right resemblance measure (for biological samples generally one that ignores joint absences) is how we deal with the inflated zero problem. That is why we use a measure like Bray-Curtis to define resemblance, rather than a measure like Euclidean distance. The zero-adjusted measure is for specific circumstances where there are few or no occurrences in samples, which is not the same thing as many zeros overall.
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